Publication
A Puristic Approach for Joint Dependency Parsing and Semantic Role Labeling.
Alexander Volokh; Günter Neumann
In: CoNLL 2008. Conference on Computational Natural Language Learning (CoNLL-2008), 12th, August 16-17, Manchester, United Kingdom, Pages 213-217, ACL, 2008.
Abstract
We present a puristic approach for combining
dependency parsing and semantic
role labeling. In a first step, a data-driven
strict incremental deterministic parser is
used to compute a single syntactic dependency
structure using a MEM trained
on the syntactic part of the CoNLL 2008
training corpus. In a second step, a cascade
of MEMs is used to identify predicates,
and, for each found predicate, to
identify its arguments and their types. All
the MEMs used here are trained only
with labeled data from the CoNLL 2008
corpus. We participated in the closed
challenge, and obtained a labeled macro
F1 for WSJ+Brown of 19.93 (20.13 on
WSJ only, 18.14 on Brown). For the syntactic
dependencies we got similar bad
results (WSJ+Brown=16.25, WSJ= 16.22,
Brown=16.47), as well as for the semantic
dependencies (WSJ+Brown=22.36,
WSJ=22.86, Brown=17.94). The current
results of the experiments suggest that
our risky puristic approach of following a
strict incremental parsing approach together
with the closed data-driven perspective
of a joined syntactic and semantic
labeling was actually too optimistic
and eventually too puristic.